Discover the latest and most impactful research on leveraging data analytics to generate business insights here. All the research listed comes from the ARF or one of its subsidiaries: The Journal of Advertising Research (JAR), the Marketing Science Institute (MSI) or the Coalition for Innovative Media Measurement (CIMM). Feel free to bookmark this page, as it will be updated periodically.
Do digital voice assistants make website navigation easier? Not necessarily, according to this MSI working paper. Researchers found that a sense of “social presence” created by such assistants can evoke social norms. And so, these devices can in fact predispose consumers to more prosocial behavior, such as donating to important causes and tipping.
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Which is more efficient when it comes to advantageous decision outcomes, marketing-mix metrics or financial metrics? Researchers in this award-winning MSI working paper investigated this question, basing their research on a behavioral framework and statistical model. On average, according to their sample, marketing-mix metrics are more effective than financial. Even so, managers seem more eager to use financial metrics, possibly, because they are easier to understand across the organization.
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Using mobile location data to improve targeting marketing is a good strategy, but the downside is that it can increase consumer concern over privacy. What’s a better way to do it? Instead of aggregating data by home location, doing so by the centroid of brand sites visited can result in good predictions, while still maintaining individual anonymity.
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It’s no surprise that new product launches often fail to meet their targets. The trick for managers is to improve their predictions for such products. They must balance the costs and benefits of many different data sources and analytic techniques in order to improve forecasting. To enhance the accuracy of predicting the market-level sales of new products, researchers Marton Varga, Anita Tusche, Paulo Albuquerque, Nadine Gier, Bernd Weber, and Hilke Plassmann, analyzed the added value of different data types. Their conclusions are illuminating.
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There are a number of different biases that can impact panel and census research and big data sets which utilize artificial intelligence (AI) or machine learning (ML). The attached Knowledge at Hand identifies over two dozen unique biases. It discusses how they might occur and what mitigation options researchers should consider to account for them.
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Data sets for STBs and Smart TVs have, up until now, been separate from one another, which has hampered the ability to accurately assess viewing habits. This data would be very useful for planning, buying and optimizing ad campaigns. Since the two are complementary, combining them can help us form scaled, granular, TV tuning data sets that are more nationally representative. Luckily, a new report from the Coalition for Innovative Media Measurement (CIMM)—a subsidiary of the ARF, outlines best practices for combining set-top box data and smart TV ACR data.
Member Only AccessEstablishing causality in the TV advertising-to-sales relationship, ideally, is studied through randomized trials, which match respondents to exposure and non-exposure groups. Given the difficulties of such experiments, analysis of observational data, alternatively, can reveal links between ad campaigns and sales. But new research shows how failure to control for variants can produce misleading signals to advertisers. Read the JAR summary.
Surveys are a necessary tool for exploring consumer behavior, attitudes and intentions. They provide valuable data to help make informed business decisions. However, quality matters and ignoring best practices impacts the validity and reliability of findings, which might make the data unusable.
Member Only AccessA key planning tool that is widely used by advertising professionals for strategic and creative guidance is seriously out of date. Researchers in Japan and South Korea propose a refresh to the Foote, Cone & Belding (FCB) grid, created in 1980, to reflect current-day products and consumers’ online purchase decisions. Read the JAR summary.
Radio advertisers have been relying on earlier research that showed that as much as one-third of their audience switch stations during advertising breaks, but researchers claim that number is misleading. Their new benchmark—about one-tenth of current estimates—could attract greater investment in this advertising medium. Read the JAR summary.